AML - Module Overview
Mafas Raheem
Data Scientist | Business Analyst
I am an academic/trainer/researcher specializing in the field of Data Science & Business Analytics with nearly 16 years of academic & industry experience. I hold an MSc in Data Science & Business Analytics and a Master of Business Administration degree and currently reading my PhD in the area of machine learning (Text Analytics/Natural Language Processing) at the Asia Pacific University of Innovation and Technology, Malaysia. I have published a significant number of indexed journal articles in the area of Machine Learning and Data Science matching the current business needs.
I am actively involved in consulting data analytics/machine learning projects for the business/retail domains. I have been involved in numerous data mining projects in Malaysia, and overseas. My knowledge in statistics along with my data mining/machine learning expertise always adds value in solving the contemporary business problems faced by SMEs in the area of market expansion. Also, I conduct training for data analysts and data science professionals in the area of machine learning, data storytelling and business analysis.
Email: raheem@apu.edu.my
Refer to “Staff Consultation Hour” on APU Apspace to book appointments.
This module will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Machine learning uses interdisciplinary techniques to create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention. You can understand the need of machine learning for various problem solving and improve the performance of the same with the study of various supervised and unsupervised learning algorithms in machine learning.
| CLO1 | Analyze the supervised and unsupervised learning techniques for a given field of study (C4, PLO2) | ||||||||||||||||||||||
| CLO2 | Demonstrate a solution obtained by appropriate machine learning models for various types of problems (A3, PLO6) | ||||||||||||||||||||||
| CLO3 | Criticize the accuracy of the proposed machine learning models (C6, PLO7) | ||||||||||||||||||||||
- Introduction to Data Science
- Introduction to Machine Learning
- Managing and Understanding data
- Numerical Prediction - Linear Regression
- Logistic Regression (LR)
- Naïve Bayes (NB)
- Regularization
- Evaluating model performance & Cross-Validation (CV)
- Support Vector Machines (SVM)
- Decision Tree (DT)
- Artificial Neural Networks (ANN)
- Ensembles
- Supervised Data Mining Techniques (Univariate Time Series Analysis)
For
the assignment, you are required to explore the application of Applied Machine
Learning (AML) techniques to a data problem from any of your preferred domains. You
may choose to study any one particular data problem, giving special
consideration to the unique properties of the problem domain, and testing one
or more methods on it.
Assignment (Related works) - 40 Marks
Assignment (Model Implementation) - 50 Marks
Assignment (Model Validation) - 10 Marks
Bruce, P. and Bruce, A. (2017). Practical Statistics for Data Scientists: 50 Essential Concepts. O'Reilly Media, Inc. ISBN-13: 978-1491952962.
Wickham, H. (2016). ggplot2: elegant graphics for data analysis. Springer. ISBN-13: 978-3319242750
Tibshirani, R., James, G., Witten, D., Hastie, T. (2017). An introduction to statistical learning-with applications in R. ISBN: 9781461471387
Please introduce yourself comprehensively including bio, education background and work experience.
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